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5fa5017479
Fixes #152025 Pull Request resolved: https://github.com/pytorch/pytorch/pull/152478 Approved by: https://github.com/justinchuby
162 lines
6.2 KiB
ReStructuredText
162 lines
6.2 KiB
ReStructuredText
TorchDynamo-based ONNX Exporter
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===============================
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.. automodule:: torch.onnx
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:noindex:
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.. contents:: :local:
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:depth: 1
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Overview
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--------
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The ONNX exporter leverages TorchDynamo engine to hook into Python's frame evaluation API
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and dynamically rewrite its bytecode into an FX Graph.
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The resulting FX Graph is then polished before it is finally translated into an ONNX graph.
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The main advantage of this approach is that the `FX graph <https://pytorch.org/docs/stable/fx.html>`_ is captured using
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bytecode analysis that preserves the dynamic nature of the model instead of using traditional static tracing techniques.
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In addition, during the export process, memory usage is significantly reduced compared to the TorchScript-enabled exporter.
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See the :doc:`memory usage documentation <onnx_dynamo_memory_usage>` for more information.
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Dependencies
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------------
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The ONNX exporter depends on extra Python packages:
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- `ONNX <https://onnx.ai>`_
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- `ONNX Script <https://microsoft.github.io/onnxscript>`_
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They can be installed through `pip <https://pypi.org/project/pip/>`_:
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.. code-block:: bash
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pip install --upgrade onnx onnxscript
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`onnxruntime <https://onnxruntime.ai>`_ can then be used to execute the model
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on a large variety of processors.
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A simple example
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----------------
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See below a demonstration of exporter API in action with a simple Multilayer Perceptron (MLP) as example:
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.. code-block:: python
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class MLPModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.fc0 = nn.Linear(8, 8, bias=True)
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self.fc1 = nn.Linear(8, 4, bias=True)
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self.fc2 = nn.Linear(4, 2, bias=True)
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self.fc3 = nn.Linear(2, 2, bias=True)
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self.fc_combined = nn.Linear(8 + 8 + 8, 8, bias=True) # Combine all inputs
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def forward(self, tensor_x: torch.Tensor, input_dict: dict, input_list: list):
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"""
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Forward method that requires all inputs:
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- tensor_x: A direct tensor input.
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- input_dict: A dictionary containing the tensor under the key 'tensor_x'.
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- input_list: A list where the first element is the tensor.
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"""
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# Extract tensors from inputs
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dict_tensor = input_dict['tensor_x']
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list_tensor = input_list[0]
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# Combine all inputs into a single tensor
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combined_tensor = torch.cat([tensor_x, dict_tensor, list_tensor], dim=1)
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# Process the combined tensor through the layers
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combined_tensor = self.fc_combined(combined_tensor)
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combined_tensor = torch.sigmoid(combined_tensor)
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combined_tensor = self.fc0(combined_tensor)
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combined_tensor = torch.sigmoid(combined_tensor)
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combined_tensor = self.fc1(combined_tensor)
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combined_tensor = torch.sigmoid(combined_tensor)
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combined_tensor = self.fc2(combined_tensor)
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combined_tensor = torch.sigmoid(combined_tensor)
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output = self.fc3(combined_tensor)
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return output
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model = MLPModel()
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# Example inputs
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tensor_input = torch.rand((97, 8), dtype=torch.float32)
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dict_input = {'tensor_x': torch.rand((97, 8), dtype=torch.float32)}
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list_input = [torch.rand((97, 8), dtype=torch.float32)]
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# The input_names and output_names are used to identify the inputs and outputs of the ONNX model
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input_names = ['tensor_input', 'tensor_x', 'list_input_index_0']
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output_names = ['output']
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# Exporting the model with all required inputs
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onnx_program = torch.onnx.export(model,(tensor_input, dict_input, list_input), dynamic_shapes=({0: "batch_size"},{"tensor_x": {0: "batch_size"}},[{0: "batch_size"}]), input_names=input_names, output_names=output_names, dynamo=True,)
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# Check the exported ONNX model is dynamic
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assert onnx_program.model.graph.inputs[0].shape == ("batch_size", 8)
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assert onnx_program.model.graph.inputs[1].shape == ("batch_size", 8)
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assert onnx_program.model.graph.inputs[2].shape == ("batch_size", 8)
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As the code above shows, all you need is to provide :func:`torch.onnx.export` with an instance of the model and its input.
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The exporter will then return an instance of :class:`torch.onnx.ONNXProgram` that contains the exported ONNX graph along with extra information.
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The in-memory model available through ``onnx_program.model_proto`` is an ``onnx.ModelProto`` object in compliance with the `ONNX IR spec <https://github.com/onnx/onnx/blob/main/docs/IR.md>`_.
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The ONNX model may then be serialized into a `Protobuf file <https://protobuf.dev/>`_ using the :meth:`torch.onnx.ONNXProgram.save` API.
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.. code-block:: python
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onnx_program.save("mlp.onnx")
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Use the same model to compare with the TorchScript-enabled exporter
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-------------------------------------------------------------------
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The biggest difference between the TorchScript-enabled exporter and the TorchDynamo-based exporter is that the latter
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requires dynamic_shapes to be the same tree structure as the input, while the former
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requires the dynamic_shapes to be a single and flatten dictionary.
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.. code-block:: python
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torch.onnx.export(model,(tensor_input, dict_input, list_input), "mlp.onnx", dynamic_axes={"tensor_input":{0: "batch_size"}, "tensor_x": {0: "batch_size"}, "list_input_index_0": {0: "batch_size"}}, input_names=input_names, output_names=output_names)
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Inspecting the ONNX model using GUI
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-----------------------------------
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You can view the exported model using `Netron <https://netron.app/>`__.
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.. image:: _static/img/onnx/onnx_dynamo_mlp_model.png
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:width: 40%
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:alt: MLP model as viewed using Netron
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When the conversion fails
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-------------------------
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Function :func:`torch.onnx.export` should called a second time with
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parameter ``report=True``. A markdown report is generated to help the user
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to resolve the issue.
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.. toctree::
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:hidden:
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onnx_dynamo_memory_usage
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API Reference
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-------------
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.. autofunction:: torch.onnx.export
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.. autoclass:: torch.onnx.ONNXProgram
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:members:
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.. autofunction:: is_in_onnx_export
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.. autoclass:: torch.onnx.OnnxExporterError
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:members:
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.. autofunction:: torch.onnx.enable_fake_mode
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Deprecated
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----------
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The following classes and functions are deprecated and will be removed.
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.. autofunction:: torch.onnx.dynamo_export
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.. autoclass:: torch.onnx.ExportOptions
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